An Ensemble Learning Model for Short-Term Passenger Flow Prediction

Abstract

In recent years, with the continuous improvement of urban public transportation capacity, citizens’ travel has become more and more convenient, but there are still some potential problems, such as morning and evening peak congestion, imbalance between the supply and demand of vehicles and passenger flow, emergencies, and social local passenger flow surged due to special circumstances such as activities and inclement weather. If you want to properly guide the local passenger flow and make a reasonable deployment of operating buses, it is necessary to grasp the changing law of public transportation short-term passenger flow. This paper builds a short-term passenger flow prediction model for urban public transportation based on the idea of integrated learning. The goal is to use the integrated model to accurately predict the short-term passenger flow of urban public transportation, using Multivariable Linear Regression (MLR), K-Nearest Neighbor (KNN), eXtreme Gradient Boosting (XGBoost), and Gated Recurrent Unit (GRU) as the four seed models, and then use regression algorithm to integrate the model and predict the passenger flow, station boarding and landing, and cross-sectional passenger flow data of the typical representative line 428 in the “Huitian Area” of Beijing from January 1, 2020, to May 31, 2020. Finally, the prediction results of the submodels are compared with those of the integrated model to verify the superiority of the integrated model. The research results of this paper can enrich the short-term passenger flow forecasting system of urban public transportation and provide effective data support and scientific basis for the passenger flow, vehicle management, and dispatch of urban public transportation.

Bus Single-Trip Time Prediction Based on Ensemble Learning

“…Ensemble learning is a widely used approach in prediction using ensemble predictive models in machine learning. It is based on the principle of integrating different sets of learners for improving prediction accuracy [ 18 ]. The dominant area of research by scholars is currently designing ensemble models that enhance weak learners to strong learners and ensemble multiple learners generated by the same algorithm [ 18 ].…”

“…It is based on the principle of integrating different sets of learners for improving prediction accuracy [ 18 ]. The dominant area of research by scholars is currently designing ensemble models that enhance weak learners to strong learners and ensemble multiple learners generated by the same algorithm [ 18 ]. In ensemble learning, the prediction accuracy is greatly improved by combining multiple learners and the ensemble model performs better than each sub-model.…”

A Comparative Analysis of Passenger Flow Forecasting in Trams Using Machine Learning Algorithms

“…Wang et al [34] used an integrated model combining multivariate linear regression, K-nearest neighbor, XGBoost, and GRU as four submodels to accurately predict urban public transportation shortterm passenger flows. They then integrated these models using a regression algorithm, demonstrating the integrated model's superiority over individual submodels.…”

Passenger Flow Prediction Using Smart Card Data from Connected Bus System Based on Interpretable XGBoost

“…Yun et al built a local optimal fusion model based on LSTM, LightGBM, and dynamic regression device [29]. Wang et al took Multivariable Linear Regression (MLR), k-nearest neighbor (KNN), XGBoost, and Gated Recurrent Unit (GRU) as four seed models to establish a regression integration model to accurately predict short-term passenger flows of urban public transport [30]. The aim of this paper is to develop a prediction using the massive bus card and bus operation data and find the importance of variables for the prediction accuracy, and therefore, XGBoost is implemented.…”